Appetite-Regulating Hormones and Eating Behaviors in Children with Autism Spectrum Disorder
Notice bibliographique
Résumé
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that involves deficits in social, behavioral, and communicative domains. As an increasing number of children are diagnosed with ASD, within Canada and globally, there has also been increased findings of higher rates of overweight and obesity among this population. Excessive weight gain is of concern due to the social, physical, and psychological impacts of obesity and its secondary associated disorders. Particularly for individuals with ASD and families, this can lead to an added burden placed onto this already vulnerable population. In order to improve the effectiveness of treatments and curb the development of overweight and obesity in ASD, a more comprehensive understanding of some of the underlying mechanisms such as possible hormonal factors and feeding behaviors is needed. Therefore, the overall objective of this research was to (1) assess the risk factors for unhealthy weight gain and obesity that have been implicated in ASD, (2) examine hormones involved in regulation of appetite and energy balance (leptin, ghrelin, GLP-1, PYY, insulin) and how they may differ based on weight status among children with ASD, and (3) to explore differences in mealtime feeding behaviors among groups of varying weight status with ASD. In chapter 2, risk factors for unhealthy weight gain and obesity were explored among children with ASD. We discussed the role of selective feeding behaviors, which are often related to sensory challenges and specific behavioral phenotypes, such as restricted and repetitive behaviors. We also discussed the research on physical activity opportunities and sedentary behaviors among this population. Parents also often report more barriers to physical exercise due to the social nature of many activities. Furthermore, we discussed the role of genetics and specific genes that have been implicated in both ASD and obesity development. In addition, many children with ASD often present with secondary comorbidities (e.g., depression), and medications to manage these symptoms can further impact weight status. We also discussed emerging factors, which we defined as factors independently associated with increased risk for both obesity and ASD, that have not yet been studied as risk factors for unhealthy weight gain and obesity among children with ASD. The latter included the gut microbiota, endocrine influences, and maternal metabolic disorders. Chapter 3 summarizes the findings of a cross-sectional study comprised of 21 children with ASD between the ages of 5 to 12 years old. Of the recruited children, 15 were of normal weight (NW) status and 6 children were of overweight or obese (OWOB) weight status. Information through anthropometric measurements, blood samples, and questionnaires was collected. The major findings of this study included that under fasting conditions, the group with OWOB weight status was found to have higher leptin concentrations (p=0.018). We also found there were higher reported feeding challenges among the OWOB group (p=0.045). The major findings of this thesis are that a combination of behavioral, lifestyle, and physiological components contribute to overweight and obesity among children with ASD. This research highlights that behavioral and hormonal factors may also contribute to accelerated weight gain among children with ASD, and there is a need for further research to clarify the interplay among these factors in order to better define potential targets for prevention and intervention strategies.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».